These days, various types of professional and consumer-level video display devices are available in the market (e.g. LCD, Liquid Crystal on Silicon (LCoS), Plasma, DLP, and CRT). Display devices include all kinds of color reproduction devices, such as film printers, projectors, direct view displays and photo printers. Manufacturers are making an effort to enhance image display qualities in terms of colors, contrast, and gamma, and often claim that their displays show a wider range of colors, higher contrast ratios, and so on. However, those colors, contrast, gammas, etc. often appear quite different on the users' displays (e.g., ‘target’ displays) as compared to those seen in reference displays which are used to create the video contents during post production. As a result, the end user displays cannot carry on the director's or creator's intended display qualities to the consumer reliably.
In the framework of the calibration of a target display, in order to reproduce colors on the target display close to how they would have been reproduced on a reference display (e.g. a film projector or a CRT), the target display and the reference display have to be characterized, leading to device models. A device model transforms input, device dependent color values into output, device independent color values. A display device can be described by its inverse device model. An inverse device model transforms output, device independent color values into input, devices dependent color values.
One method to ensure good calibration quality is to perform a visual comparison between the reference and the target display with multiple numbers of human subjects. However, this is a very time consuming and extremely costly approach.
Another approach is to calculate objective error measures for the quality of the reference device model and the target device model. However, the quality of an inverse device model is often evaluated in a device dependent color space which does not allow an objective assessment. If quality is evaluated in device independent, absolute color spaces, color transforms with inherent errors are needed so that the resultant quality does not only depend on the device model to be evaluated.
FIG. 1 depicts a general color management workflow for calibrating a target display device. As shown in FIG. 1, the characterization of a display usually includes color measurement and the building of two display models: the reference display forward model 105, and the target display inverse model 107 from measured colors. Measured colors consist of a first set of device dependent input colors and a first set of corresponding responsive device independent output colors. The display forward model 105 is able to transform device dependent display input colors (e.g., an image from a reference display 101) into device independent display output colors (e.g., absolute color values). The target display inverse model is able to the transform device independent display output colors (absolute color values) into device dependent colors input to a target display 109.
Calibration 102 of a target display is often done by transforming all colors to be displayed first by a forward reference device model 105 and then by an inverse target device model 107.
One approach for quality evaluation is to calculate objective error measures for the forward reference device model and the inverse target device model. To do so, usually second measured colors are used for each display consisting of a second set of device dependent input colors and a second set of corresponding responsive device independent output colors.
For verification of the forward reference model, the second set of device dependent input colors of the reference display device are passed through the forward reference model, resulting in a third set of responsive, device independent output colors. The difference between the second set of device independent output colors and the third set of device independent output colors indicates the quality of the forward reference device model in device independent color space. This quality evaluation is in device independent, absolute color space and therefore sufficient (i.e. errors are correlated with human visual judgment and with radiometric measurement).
For verification of the inverse target model, the second set of device independent output colors of the target device is passed through the inverse target device model resulting in a third set of device dependent input color values. The difference between the third set of device dependent input colors and the second set of device dependent input colors of the target device indicates the quality of the inverse target model in device dependent color space. This quality evaluation is in device dependent color space and therefore not sufficient (i.e. errors are not correlated with the human visual judgment and depend on the target device).
In order to calculate the characterization error of the target device in device independent, absolute color space, one known method applies the following procedure. That is, FIG. 2 depicts a first exemplary quality evaluation method. As shown in FIG. 2, the device dependent input colors to be tested 203 (i.e., the second set of device dependent input colors), are transformed into device independent, absolute colors 207 (i.e., the second set of device independent, output colors). These colors are transformed by the inverse target device model to be tested into device dependent input colors to be tested 203. For color transform into device independent color space, the forward target device model 105 is used. Then, the color differences are evaluated between the transformed device independent colors 207 and the measured device independent colors 205, the second set of device independent output colors 205, indicating the quality of the inverse target device model 107. However, unfortunately, using the forward device model has inherent errors. For example, since the forward device model is calculated from the measured colors of the target display 109, it has certain errors such as measurement errors, characterization modeling errors, etc. Further, the quality indicated by the color differences is the quality of both the inverse and the forward models. This known method fails to evaluate precisely the error of only the inverse target display device model 107.
Another exemplary quality evaluation method is depicted in FIG. 3. As shown in FIG. 3, both the device and dependent input colors to be tested 203 and the second set of device dependent input colors of the target display 201 are input into a target display 109. Measuring these colors leads to a first and second set of measured, absolute, device independent colors, respectively. The second set of measured, absolute, device independent colors is input in the inverse target device model 107 to be tested resulting in the mentioned device dependent input colors to be tested 203. Then, the color differences are evaluated between the first and second set of device independent colors (205, 209) indicating the quality of the inverse target device model 107. However, this measurement process has inherent errors from the measurement process of measured transformed device independent colors 205, for example measurement noise. Also, the quality indicated by the color differences is the quality of both the inverse model and the measurement process. This known method also fails to evaluate precisely the error of only the inverse target display device model.
It is to be noted that forward and inverse device models are rarely perfect inverse operators but contain inherent errors, some of which follow:                Out-of-gamut colors: Some of the device independent colors can be outside the color gamut of the device. There are no device-dependent color values for those device independent colors. The inverse device model is not defined for those device independent colors. Usually, those device independent colors need to be mapped into the gamut color gamut of the device. This color mapping causes an error.        Out-of-range colors: Some of the device independent colors can be outside the dynamic range of the device. Their amplitude is either too weak (too dark) or too intense (too luminous). There are no device-dependent color values for those device independent colors. The inverse device model is not defined for those device independent colors. Usually, those device independent colors need to be mapped into the dynamic range of the device. This tone mapping causes an error.        Quantization: Device-dependent color values are quantized in current video systems. Quantization is a lossy operation with no existing inverse operation. For this reason, the inverse device model cannot generally compensate for (or cannot invert) the quantization. Thus, quantization will cause errors in the model. Also, device independent color values are often quantized when calculating on a computer the combination of an inverse and a forward, or of a forward and an inverse model.        Noise: When the device is controlled using device-dependent color values, its electronic and optical circuits will add noise to the signals representing these color values. This effect cannot be inverted and considered in the inverse device model since the noise is stochastic and its instant realization is unknown. Noise will cause reproduction errors.        Model errors: The inverse device model (or any other transform) is usually based on a more or less complicate mathematical model. This model can be simplified or simply not perfect with respect to the inverse device model (or any color reproduction process) that is to be modeled. Furthermore, when building the inverse model from a model (for example for building the inverse device model from a forward model or the forward model from an inverse model), the inverse model combined with the model often not give a neutral operation for reasons of simplicity, numerical precision or linearity of one or both models. This causes model errors.        